Cost-Effective Defect Detection in Bonded Glass Element Modules

2009 ◽  
Vol 6 (4) ◽  
pp. 30-34
Author(s):  
Michael F. Zäh ◽  
Christian Thiemann ◽  
Stefan Böhm ◽  
Christian Srajbr ◽  
Christian Lammel ◽  
...  
Keyword(s):  
Defect Detection ◽  
Cost Effective ◽  
Glass Element

A Kriging Based Fast and Efficient Method for Defect Detection in Massive Pipelines Using Magnetic Flux Leakages

2020 ◽  
Author(s):  
Subrata Mukherjee ◽  
Xuhui Huang ◽  
Lalita Udpa ◽  
Yiming Deng
Keyword(s):  
Magnetic Flux ◽  
Defect Detection ◽  
Synthetic Data ◽  
Cost Effective ◽  
Error Rates ◽  
Lattice Points ◽  
Operating Characteristics ◽  
Wide Range ◽  
Similar Accuracy ◽  
Fast Procedure

Abstract Systems in service continue to degrade with passage of time. Pipelines are among the most common systems that wear away with usage. For public safety it is of utmost importance to monitor pipelines and detect new defects within the pipelines. Magnetic flux leakage (MFL) testing is a widely used nondestructive evaluation (NDE) technique for defect detections within the pipelines, particularly those composed of ferromagnetic materials. Pipeline inspection gauge (PIG) procedure based on line-scans or 2D-scans can collect accurate MFL readings for defect detection. However, in real world applications involving large pipe-sectors such extensive scanning techniques are extremely time consuming and costly. In this paper, we develop a fast and cheap methodology that does not need MFL readings at all the points used in traditional PIG procedures but conducts defect detection with similar accuracy. We consider an under-sampling based scheme that collects MFL at uniformly chosen random scan-points over large lattices instead of extensive PIG scans over all lattice points. Based on readings for the chosen random scan points, we use Kriging to reconstruct MFL readings over the entire pipe-sectors. Thereafter, we use thresholding-based segmentation on the reconstructed data for detecting defective areas. We demonstrate the applicability of our methodology on synthetic data generated using popular finite element models as well as on MFL data collected via laboratory experiments. In these experiments spanning a wide range of defect types, our proposed novel MFL based NDE methodology is witnessed to have operating characteristics within the acceptable threshold of PIG based traditional methods and thus provide an extremely cost-effective, fast procedure with competing error rates that can be successfully used for scanning massive pipeline sectors.

scholarly journals Road Defect Detection in Street View Images using Texture Descriptors and Contour Maps

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
David Abou Chacra ◽  
Henry Leopold ◽  
Jeremy Pinto ◽  
Norman Lunscher ◽  
Georges Younes ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Defect Detection ◽  
Cost Effective ◽  
Contour Map ◽  
Contour Maps ◽  
Street View ◽  
Effective System ◽  
Crucial Part ◽  
Municipal Level ◽  
Novel Algorithm

Road quality assessment is a crucial part in municipalities’ workto maintain their infrastructure, plan upgrades, and manage theirbudgets. Properly maintaining this infrastructure relies heavily onconsistently monitoring its condition and deterioration over time.This can be a challenge, especially in larger towns and cities wherethere is a lot of city property to keep an eye on. We review roadquality assessment methods currently employed, and then describeour novel algorithm aimed at identifying distressed road regionsfrom street view images and pinpointing cracks within them. Wepredict distressed regions by computing Fisher vectors on localSIFT descriptors and classifying them with an SVM trained to distinguishbetween road qualities. We follow this step with a comparisonto a weighed contour map within these distressed regionsto identify exact crack and defect locations, and use the contourweights to predict the crack severity. Promising results are obtainedon our manually annotated dataset, which indicate the viability ofusing this cost-effective system to perform road quality assessmentat a municipal level.

scholarly journals Lychee Surface Defect Detection Based on Deep Convolutional Neural Networks with GAN-Based Data Augmentation

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1500
Author(s):  
Chenglong Wang ◽  
Zhifeng Xiao
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Data Augmentation ◽  
Cost Effective ◽  
Deep Convolutional Neural Networks ◽  
Object Boundary ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Original Dataset ◽  
Surface Defect Detection

The performance of fruit surface defect detection is easily affected by factors such as noisy background and foliage occlusion. In this study, we choose lychee as a fruit type to investigate its surface quality. Lychees are hard to preserve and have to be stored at low temperatures to keep fresh. Additionally, the surface of lychees is subject to scratches and cracks during harvesting/processing. To explore the feasibility of the automation of defective surface detection for lychees, we build a dataset with 3743 samples divided into three categories, namely, mature, defects, and rot. The original dataset suffers an imbalanced distribution issue. To address it, we adopt a transformer-based generative adversarial network (GAN) as a means of data augmentation that can effectively enhance the original training set with more and diverse samples to rebalance the three categories. In addition, we investigate three deep convolutional neural network (DCNN) models, including SSD-MobileNet V2, Faster RCNN-ResNet50, and Faster RCNN-Inception-ResNet V2, trained under different settings for an extensive comparison study. The results show that all three models demonstrate consistent performance gains in mean average precision (mAP), with the application of GAN-based augmentation. The rebalanced dataset also reduces the inter-category discrepancy, allowing a DCNN model to be trained equally across categories. In addition, the qualitative results show that models trained under the augmented setting can better identify the critical regions and the object boundary, leading to gains in mAP. Lastly, we conclude that the most cost-effective model, SSD-MobileNet V2, presents a comparable mAP (91.81%) and a superior inference speed (102 FPS), suitable for real-time detection in industrial-level applications.

A Kriging based fast and efficient method for defect detection in massive pipelines using magnetic flux leakages

2021 ◽  
pp. 1-29
Author(s):  
Subrata Mukherjee ◽  
Xuhui Huang ◽  
Lalita Udpa ◽  
Yiming Deng
Keyword(s):  
Magnetic Flux ◽  
Defect Detection ◽  
Synthetic Data ◽  
Cost Effective ◽  
Error Rates ◽  
Lattice Points ◽  
Operating Characteristics ◽  
Wide Range ◽  
Similar Accuracy ◽  
Fast Procedure

Abstract Systems in service continue to degrade with passage of time. Pipelines are among the most common systems that wear away with usage. Magnetic flux leakage (MFL) testing is a widely used non-destructive evaluation (NDE) technique for defect detections within the pipelines, particularly those composed of ferromagnetic materials. Pipeline inspection gauge (PIG) procedure based on line-scans can collect accurate MFL readings for defect detection. However, in real world applications involving large pipe-sectors such extensive scanning techniques are extremely time consuming and costly. In this paper, we develop a fast and cheap methodology that does not need MFL readings at all the points used in traditional PIG procedures but conducts defect detection with similar accuracy. We consider an under-sampling based scheme that collects MFL at uniformly chosen random scan-points over large lattices instead of extensive PIG scans over all lattice points. Based on readings for the chosen random scan points, we use Kriging to reconstruct MFL readings. Thereafter, we use thresholding-based segmentation on the reconstructed data for detecting defective areas. We demonstrate the applicability of our methodology on synthetic data generated using finite element models as well as on MFL data collected via laboratory experiments. In these experiments spanning a wide range of defect types, our proposed novel MFL based NDE methodology is witnessed to have operating characteristics within the acceptable threshold of PIG based traditional methods and thus provide an extremely cost-effective, fast procedure with competing error rates.

scholarly journals Defect Classification of Green Plums Based on Deep Learning

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6993
Author(s):  
Haiyan Zhou ◽  
Zilong Zhuang ◽  
Ying Liu ◽  
Yang Liu ◽  
Xiao Zhang
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Surface Defects ◽  
Recognition Accuracy ◽  
Economic Value ◽  
Recognition Rate ◽  
Cost Effective ◽  
Test Time ◽  
Training Set

The green plum is rich in amino acids, lipids, inorganic salts, vitamins, and trace elements. It has high nutritional value and medicinal value and is very popular among Chinese people. However, green plums are susceptible to collisions and pests during growth, picking, storage, and transportation, causing surface defects, affecting the quality of green plums and their products and reducing their economic value. In China, defect detection and grading of green plum products are still performed manually. Traditional manual classification has low accuracy and high cost, which is far from meeting the production needs of green plum products. In order to improve the economic value of green plums and their products and improve the automation and intelligence level of the product production process, this study adopted deep learning methods based on a convolutional neural network and cost-effective computer vision technology to achieve efficient classification of green plum defects. First, a camera and LEDs were used to collect 1240 green plum images of RGB, and the green plum experimental classification standard was formulated and divided into five categories, namely, rot, spot, scar, crack, and normal. Images were randomly divided into a training set and test set, and the number of images of the training set was expanded. Then, the stochastic weight averaging (SWA) optimizer and w-softmax loss function were used to improve the VGG network, which was trained and tested to generate a green plum defect detection network model. The average recognition accuracy of green plum defects was 93.8%, the test time for each picture was 84.69 ms, the recognition rate of decay defect was 99.25%, and the recognition rate of normal green plum was 95.65%. The results were compared with the source VGG network, resnet18 network, and green lemon network. The results show that for the classification of green plum defects, the recognition accuracy of the green plum defect detection network increased by 9.8% and 16.6%, and the test speed is increased by 1.87 and 6.21 ms, respectively, which has certain advantages.

The application of electron microscopy to diagnosis in gynecologic neoplasms and tumor-like conditions

1984 ◽  
Vol 42 ◽  
pp. 102-105
Author(s):  
Lawrence M. Roth
Keyword(s):  
Electron Microscopy ◽  
Reproductive Tract ◽  
Malignant Tumors ◽  
Frozen Section ◽  
Cost Effective ◽  
Female Reproductive Tract ◽  
Ultrastructural Examination ◽  
Specific Diagnosis ◽  
Potential Value ◽  
Gynecologic Neoplasms

The female reproductive tract may be the site of a wide variety of benign and malignant tumors, as well as non-neoplastic tumor-like conditions, most of which can be diagnosed by light microscopic examination including special stains and more recently immunoperoxidase techniques. Nevertheless there are situations where ultrastructural examination can contribute substantially to an accurate and specific diagnosis. It is my opinion that electron microscopy can be of greatest benefit and is most cost effective when applied in conjunction with other methodologies. Thus, I have developed an approach which has proved useful for me and may have benefit for others. In cases where it is deemed of potential value, glutaraldehyde-fixed material is obtained at the time of frozen section or otherwise at operation. Coordination with the gynecologic oncologist is required in the latter situation. This material is processed and blocked and is available if a future need arises.

The IBM-PC in electron microscopy

1996 ◽  
Vol 54 ◽  
pp. 612-613
Author(s):  
James F. Mancuso
Keyword(s):  
Head Start ◽  
Image Acquisition ◽  
Cost Effective ◽  
Financial Applications ◽  
Number Of Factors ◽  
Instrument Control ◽  
The Cost ◽  
Training And Support ◽  
Control Image ◽  
Ibm Pc

IBM PC compatible computers are widely used in microscopy for applications ranging from control to image acquisition and analysis. The choice of IBM-PC based systems over competing computer platforms can be based on technical merit alone or on a number of factors relating to economics, availability of peripherals, management dictum, or simple personal preference.IBM-PC got a strong “head start” by first dominating clerical, document processing and financial applications. The use of these computers spilled into the laboratory where the DOS based IBM-PC replaced mini-computers. Compared to minicomputer, the PC provided a more for cost-effective platform for applications in numerical analysis, engineering and design, instrument control, image acquisition and image processing. In addition, the sitewide use of a common PC platform could reduce the cost of training and support services relative to cases where many different computer platforms were used. This could be especially true for the microscopists who must use computers in both the laboratory and the office.

A Review of 21 iPad Applications for Augmentative and Alternative Communication Purposes

2012 ◽  
Vol 21 (2) ◽  
pp. 60-71 ◽  
Author(s):  
Ashley Alliano ◽  
Kimberly Herriger ◽  
Anthony D. Koutsoftas ◽  
Theresa E. Bartolotta
Keyword(s):  
Augmentative And Alternative Communication ◽  
Cost Effective ◽  
Alternative Form ◽  
Alternative Communication ◽  
Text To Speech ◽  
Reference Guide ◽  
Expressive Communication ◽  
Communication Needs ◽  
User Friendly ◽  
Ipad Applications

Abstract Using the iPad tablet for Augmentative and Alternative Communication (AAC) purposes can facilitate many communicative needs, is cost-effective, and is socially acceptable. Many individuals with communication difficulties can use iPad applications (apps) to augment communication, provide an alternative form of communication, or target receptive and expressive language goals. In this paper, we will review a collection of iPad apps that can be used to address a variety of receptive and expressive communication needs. Based on recommendations from Gosnell, Costello, and Shane (2011), we describe the features of 21 apps that can serve as a reference guide for speech-language pathologists. We systematically identified 21 apps that use symbols only, symbols and text-to-speech, and text-to-speech only. We provide descriptions of the purpose of each app, along with the following feature descriptions: speech settings, representation, display, feedback features, rate enhancement, access, motor competencies, and cost. In this review, we describe these apps and how individuals with complex communication needs can use them for a variety of communication purposes and to target a variety of treatment goals. We present information in a user-friendly table format that clinicians can use as a reference guide.

Teletinnitus Program at the VA Gulf Coast Veterans Health Care System

2014 ◽  
Vol 4 (1) ◽  
pp. 23-29
Author(s):  
Constance Hilory Tomberlin
Keyword(s):  
Health Care ◽  
Patient Satisfaction ◽  
Patient Care ◽  
Health Care System ◽  
Gulf Coast ◽  
New Technology ◽  
Cost Effective ◽  
Veterans Health ◽  
Potential Benefits ◽  
Care System

There are a multitude of reasons that a teletinnitus program can be beneficial, not only to the patients, but also within the hospital and audiology department. The ability to use technology for the purpose of tinnitus management allows for improved appointment access for all patients, especially those who live at a distance, has been shown to be more cost effective when the patients travel is otherwise monetarily compensated, and allows for multiple patient's to be seen in the same time slots, allowing for greater access to the clinic for the patients wishing to be seen in-house. There is also the patient's excitement in being part of a new technology-based program. The Gulf Coast Veterans Health Care System (GCVHCS) saw the potential benefits of incorporating a teletinnitus program and began implementation in 2013. There were a few hurdles to work through during the beginning organizational process and the initial execution of the program. Since the establishment of the Teletinnitus program, the GCVHCS has seen an enhancement in patient care, reduction in travel compensation, improvement in clinic utilization, clinic availability, the genuine excitement of the use of a new healthcare media amongst staff and patients, and overall patient satisfaction.

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